Implement torch/nn neural network library for OCC

[Copilot]

Adds complete Lua/Torch neural network library implementing the torch/nn API for cognitive architecture integration.

Implementation

Core Framework (3 base classes)

• Module - Forward/backward propagation, parameter management, type conversion
• Container - Composite module orchestration
• Criterion - Loss function interface

Layers & Activations (7 modules)

• Linear - Fully connected with gradient computation
• Activations: Tanh, Sigmoid, ReLU, SoftMax, LogSoftMax

Containers (3 types)

• Sequential - Pipeline composition
• Parallel - Input slicing with concatenation
• Concat - Multi-input aggregation

Loss Functions

• MSECriterion, ClassNLLCriterion, CrossEntropyCriterion

Training

• StochasticGradient - SGD with learning rate decay

Integration Points

• AtomSpace: Networks stored as hypergraph structures, parameters as FloatValues
• PLN: Neural outputs feed symbolic inference chains
• MOSES: Architecture search via evolutionary algorithms
• ECAN: Attention-weighted gradient updates
• a9nn: Foundation for cognitive agent framework (see .github/agents/a9nn.md)

Usage

require('nn')

local net = nn.Sequential()
   :add(nn.Linear(784, 128))
   :add(nn.ReLU())
   :add(nn.Linear(128, 10))
   :add(nn.LogSoftMax())

local criterion = nn.ClassNLLCriterion()
local trainer = nn.StochasticGradient(net, criterion)
trainer.learningRate = 0.01
trainer:train(dataset)

Files

• 17 Lua modules (~1,325 LOC)
• 4 documentation files (README, API reference, integration guide, implementation summary)
• Test suite and examples
• CMakeLists.txt updated with BUILD_NN option

Standard torch/nn API compatibility maintained. Ready for hybrid neuro-symbolic reasoning.

Original prompt

implement https:/github.com/torch/nn

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[danregima]

cooool